Textural Features for Image Classification

Robert M. Haralick, Its'hak Dinstein, K. Shanmugam

Research output: Contribution to journalArticlepeer-review

17241 Scopus citations

Abstract

Texture is one of the important characteristics used in identifying objects or regions of interest in an image, whether the image be a photomicrograph, an aerial photograph, or a satellite image. This paper describes some easily computable textural features based on graytone spatial dependancies, and illustrates their application in category-identification tasks of three different kinds of image data: Photomicrographs of five kinds of sandstones, 1:20 000 panchromatic aerial photographs of eight land-use categories, and Earth Resources Technology Satellite (ERTS) multispecial imagery containing seven land-use categories. We use two kinds of decision rules: One for which the decision regions are convex polyhedra (a piecewise linear decision rule), and one for which the decision regions are rectangular parallelpipeds (a min-max decision rule). In each experiment the data set was divided into two parts, a training set and a test set. Test set identification accuracy is 89 percent for the photomicrographs, 82 percent for the aerial photographic imagery, and 83 percent for the satellite imagery. These results indicate that the easily computable textural features probably have a general applicability for a wide variety of image-classification applications.

Original languageEnglish
Pages (from-to)610-621
Number of pages12
JournalIEEE Transactions on Systems, Man and Cybernetics
VolumeSMC-3
Issue number6
DOIs
StatePublished - 1 Jan 1973
Externally publishedYes

ASJC Scopus subject areas

  • Engineering (all)

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